import operator_benchmark as op_bench
import torch

tensor_conversion_short_configs = op_bench.cross_product_configs(
    M=(
        8,
        16,
        32,
    ),
    N=(
        16,
        64,
        128,
    ),
    device=["cpu", "cuda"],
    tags=["short"],
)

tensor_conversion_long_configs = op_bench.cross_product_configs(
    M=(
        64,
        128,
        256,
        512,
    ),
    N=(
        256,
        512,
        1024,
        2048,
    ),
    device=["cpu", "cuda"],
    tags=["long"],
)


class FloatToHalfTensorConversionBenchmark(op_bench.TorchBenchmarkBase):
    def init(self, M, N, device):
        self.inputs = {
            "input": torch.rand(
                M, N, device=device, requires_grad=False, dtype=torch.float
            )
        }

    def forward(self, input):
        return input.to(torch.half)


class HalfToFloatTensorConversionBenchmark(op_bench.TorchBenchmarkBase):
    def init(self, M, N, device):
        self.inputs = {
            "input": torch.rand(
                M, N, device=device, requires_grad=False, dtype=torch.half
            )
        }

    def forward(self, input):
        return input.to(torch.float)


op_bench.generate_pt_test(
    tensor_conversion_short_configs, FloatToHalfTensorConversionBenchmark
)
op_bench.generate_pt_test(
    tensor_conversion_long_configs, FloatToHalfTensorConversionBenchmark
)
op_bench.generate_pt_test(
    tensor_conversion_short_configs, HalfToFloatTensorConversionBenchmark
)
op_bench.generate_pt_test(
    tensor_conversion_long_configs, HalfToFloatTensorConversionBenchmark
)

if __name__ == "__main__":
    op_bench.benchmark_runner.main()
